• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.344-347
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• 2004

Critical cryptography applications require the production of an unpredictable and unbiased stream of binary data derived from a fundamental noise mechanism. In this paper, we proposed a RNG with Gaussian noise using filter algorithm. The proposed scheme is designed to reduce the statistical property of the biased bit stream in the output of a RNG. Experimental show that we analysis the loss rate according to window size and propose optimum window size.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.11
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• pp.1749-1755
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• 2010

The performance of power window system was decided by driving characteristics of the window regulator part and reaction by the glass run. The performance of power window system usually has been predicted by experimental methods. In this paper, an analytical method using the explicit code was suggested to overcome the limit of the experimental methods. The friction coefficient of glass run was obtained by the friction test at various conditions and the Mooney-Rivlin model was used. Also, a mechanism of window regulator consisted of the fast belt system and the slip ring elements. And, we conducted the analysis considering characteristic of a motor and obtained the lifting speed of automotive glass with high reliability

• Proceedings of the Korean Information Science Society Conference
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• 2001.10c
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• pp.10-12
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• 2001

와이드 이슈 슈퍼스칼라 프로세서에서 값 예측기는 한 명령어의 결과를 미리 예측하여 명령들 간의 데이터 종속관계를 극복하고 실행함으로써 명령어 수준 병렬성(Instruction Level Parallesim ILP)을 향상시키는 기법이다. 본 논문에서는 명령어 수준 병렬성을 이용하여 성능을 향상시키기 위하여 데이터 값을 미리 예측하여 병렬로 이슈하고 수행하는 값 예측기의 성능을 비교분석 한다. 먼저 값 예측기 종류별로 성능을 측정한다 그리고 테이블의 갱신시점, 트레이스 캐시 유무 및 명령윈도우 크기에 따른 값 예측기의 성능영향을 평가분석 한다. 성능분석 결과 최근 값 예측기가 간소한 하드웨어 구성에도 불구하고 우수한 성능을 보였다. 그리고 예측테이블 갱신시점과 트레이스캐시의 사용이 값 예측기의 성능향상에 영향을 주었다.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.8
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• pp.25-32
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• 2012

In this paper, we propose a method for improving the performance of low complexity LS channel estimation for OFDM in fast time varying channels. The CE-BEM channel model used for the low complexity LS channel estimation has a problem on its own and deteriorates channel estimation performance. In this paper, we first use time domain windowing in order to remove the effect of ICI caused by data symbols. Then samples are taken from the results of the LS channel estimation and the effects of the windowing are removed from them. For resolving the defect of CE-BEM, the channel responses are recovered by interpolating the resultant samples with DPSS employed as basis functions the characteristics of which is well matched to the time variation of the channel. Computer simulations show that the proposed channel estimation method gives rise to performance improvement over conventional methods especially when channel variation is very fast and confirm that not only which type of functions is selected for the basis but how many functions are used for the basis is another key factor to performance improvement.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.483-492
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• 2007

Time series of conventional prediction techniques uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.12
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• pp.1408-1418
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• 2007

In this paper, we analyze the performance of pilot symbol assisted channel estimation and equalization schemes for OFDM systems over frequency-selective time-varying channels and propose methods to improve the system performance. In the least square(LS) and linear minimum mean square error(MMSE) channel estimation, time domain windowing is introduced for banding the frequency domain channel matrix. The linear MMSE and decision feedback equalization schemes are employed with the pilot symbols for channel estimation taken into account in the equalization process. To reduce computational complexity, the band LU matrix factorization algorithm is introduced in solving the linear systems involved in the equalization, and the performances are compared with the known previous results by computer simulations. When time domain windowing is employed in the decision feedback equalization, the matrix related with the decision feedback process is shown to be unhanded and the resultant performance degradation is analyzed.

• Journal of KIISE:Databases
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• v.37 no.1
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• pp.1-11
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• 2010

Recently, there has been a growing interest in the processing of continuous queries over multiple data streams. When the arrival rates of tuples exceed the memory capacity of the system, a load shedding technique is used to avoid the system becoming overloaded by dropping some subset of input tuples. In this paper, we propose an effective load shedding algorithm for multi-way windowed joins over multiple data streams. Most previous load shedding algorithms estimate the productivity of each tuple, i.e., the number of join output tuples produced by the tuple, based on its "join attribute value" and drop tuples with the lowest productivity. However, the productivity of a tuple cannot be accurately estimated from its join attribute value when the join attribute values are unique and do not repeat, or the distribution of the join attribute values changes over time. For these cases, we estimate the productivity of a tuple based on its "arrival order" on data streams, rather than its join attribute value. The proposed method can effectively estimate the productivity of a tuple even when the productivity of a tuple cannot be accurately estimated from its join attribute value. Through extensive experiments and analysis, we show that our proposed method outperforms the previous methods in terms of effectiveness and efficiency.

• The Journal of the Acoustical Society of Korea
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• v.19 no.3
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• pp.51-56
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• 2000

Is this paper, we studied on the automatic speech control system in real-time windows environment using voice recognition. The applied reference pattern is the variable DMS model which is proposed to fasten execution speed and the one-stage DP algorithm using this model is used for recognition algorithm. The recognition vocabulary set is composed of control command words which are frequently used in windows environment. In this paper, an automatic speech period detection algorithm which is for on-line voice processing in windows environment is implemented. The variable DMS model which applies variable number of section in consideration of duration of the input signal is proposed. Sometimes, unnecessary recognition target word are generated. therefore model is reconstructed in on-line to handle this efficiently. The Perceptual Linear Predictive analysis method which generate feature vector from extracted feature of voice is applied. According to the experiment result, but recognition speech is fastened in the proposed model because of small loud of calculation. The multi-speaker-independent recognition rate and the multi-speaker-dependent recognition rate is 99.08% and 99.39% respectively. In the noisy environment the recognition rate is 96.25%.

• Journal of KIISE
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• v.44 no.8
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• pp.782-790
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• 2017

The service, 'Hot Search Keywords', provides a list of the most hot search terms of different web services such as Naver or Daum. The service, bases the changes in rank of a specific search keyword on changes in its users' interest. This paper introduces a temporal modelling framework for predicting the rank change of hot search keywords using past rank data and machine learning. Past rank data shows that more than 70% of hot search keywords tend to disappear and reappear later. The authors processed missing rank value, using deletion, dummy variables, mean substitution, and expectation maximization. It is however crucial to calculate the optimal window size of the past rank data. We proposed an optimal window size selection approach based on the minimum amount of time a topic within the same or a differing context disappeared. The experiments were conducted with four different machine-learning techniques using the Naver, Daum, and Nate 'Hot Search Keywords' datasets, which were collected for 2 years.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.201-213
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• 2006

A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interpolation, we first decide the sites of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of query sequences to be peformed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subsequence matchings. Based on this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally Prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of extensive experiments with a real-life stock data set and a large volume of a synthetic data set. The results reveal that the proposed approach improves the previous one by 1.5 to 7.8 times.